Python for Business: Instructor Notes

Overall

This lesson is written as an introduction to Python, but its real purpose is to introduce the single most important idea in programming: how to solve problems by building functions, each of which can fit in a programmer’s working memory. In order to teach that, we must teach people a little about the mechanics of manipulating data with lists and file I/O so that their functions can do things they actually care about. Our teaching order tries to show practical uses of every idea as soon as it is introduced; instructors should resist the temptation to explain the “other 90%” of the language as well.

Since these lessons are voiced towards business students with previous experience using spreadsheets, it is frequently helpful to preface your discussion of any new topic with a reference to the spreadsheet operation. By showing the Excel analogue first, many students more quickly grasp what is being taught because they have a way to analogize around the problem.

1. Show (or ask) how to carry out a task using Excel.
2. Show the task in Python.
3. Extend the task using Python.
4. Ask how to carry out the extension in Excel—this is often not straightforward, particularly as tasks get more complex.

Explain that we use Python because:

• It’s free.
• It has a lot of scientific libraries, and more are constantly being added.
• It has a large scientific user community.
• It’s easier for novices to learn than most of the mature alternatives.

Watching the instructor grow programs step by step is as helpful to learners as anything to do with Python. Resist the urge to update a single cell repeatedly (which is what you’d probably do in real life). Instead, clone the previous cell and write the update in the new copy so that learners have a complete record of how the program grew. Once you’ve done this, you can say, “Now why don’t we just break things into small functions right from the start?”

The discussion of command-line scripts assumes that students understand standard I/O and building filters, which are covered in the lesson on the shell.

Frequently Argued Issues (FAI)

• import ... as ... syntax.

  This syntax is commonly used in the scientific Python community; it is explicitly recommended in documentation to import numpy as np and import matplotlib.pyplot as plt. Despite that, we have decided not to introduce aliasing imports in this novice lesson due to the additional cognitive load it puts on students, despite the typing that it saves. A good summary of arguments for and against can be found in PR #61.

  It is up to you as an individual instructor whether you want to introduce these aliases when you teach this lesson, but we encourage you to please read those arguments thoroughly before deciding one way or the other.

• NumPy methods.

  We used to use NumPy array methods in the first NumPy topic. We switched these methods to the equivalent functions because a majority of instructors supported the change; see PR #244 for detailed arguments for and against the change.

• Underscores vs. hyphens in filenames

  We used to use hyphens in filenames in order to signify that these Python files should only be run as scripts and never imported. However, after some discussion, including an informal Twitter poll, we switched over to underscores because many files that start off as Python scripts end up being imported eventually. For that reason, we also added if __name__ == '__main__' guards around main() calls, which is how real-world Python scripts ensure that imports do not result in side-effects.

After discussing the challenges is a good time to introduce the b *= 2 syntax.